Association between blue light exposure from digital devices and dry eye syndrome in young adults: a systematic review
Bibliographic record
Abstract
Background: The rise in digital device usage has amplified exposure to blue light, raising concerns regarding its impact on ocular health, particularly in relation to dry eye syndrome (DES). This systematic review aims to assess the association between blue light exposure and DES severity among young adults, with a focus on studies demonstrating significant findings and minimal risk of bias. Objective: This review examines the impact of blue light exposure on DES in young adults, focusing on symptom severity and the potential benefits of blue light filters. Material and Method: A comprehensive search was conducted in PubMed, Scopus, and Google Scholar, covering the period from 2015 to 2025. Out of 20 identified studies, eight met the inclusion criteria. Data extraction focused on blue light exposure, DES assessment, and effect sizes. Risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). Conclusion: Blue light exposure is significantly associated with increased DES symptoms in young adults. Implementing screen time management strategies and blue light filters may mitigate ocular surface damage. Further longitudinal studies are necessary to validate preventive strategies and assess long-term outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".